If most organizations are aware of the new European legislation GDPR few have thought through how this framework will change the practice of Data Science

This week’s news in the US, in Europe, and in the Middle East is a vivid reminder of the fallacy of rational decision-making. Whether we are reading about business, economics or society, each day seems to bring its load of conspicuously poor decision-making.

The UPPA closed its international week in Bayonne last Friday on the theme of Industry 4.0. In front of one hundred thirty participants from two dozen universities on three continents, we had the opportunity to discuss the current realities and future perspectives of digital transformation in business.

How exactly do you land that dream job in Data Science? Since these are high profile, high paying opportunities, most companies won’t be relying on resume robots to sift through the quagmire of candidates. The LinkedIn Group “Analytics for Management” is currently examining the critical success factors of successful employment in data science.

In a world that privileges novelty, modernity, and innovation, analytics isn’t exactly a passing fad. Mankind has been trying to make sense of the world around him since at least the beginning of recorded history. Most of science, if not the arts, has been based on scanning the environment, qualifying the data at hand, choosing the best method to move forward, and transforming our impressions into individual and collective action.

Michel Foucault once argued that the only viable yardstick for measuring technology was whether or not it contributed to human potential. The near future of Health Analytics may provide substantive proof of this vision. By 2020, roughly 25,000 petabytes of patient data will be available to the industry.[i] KPMG’s recent survey of healthcare professionals reveals that fifty-six percent of the participants surveyed believe that this data will largely contribute to our practice of business intelligence, while 35 percent cite lowering healthcare costs, and 32 percent suggest improved health outcomes.[ii] As healthcare organizations invest heavily in technology and analytics to take advantage of these opportunities, what are the opportunities for aspiring data scientists?

How can data spur innovation?

If management is about reducing risk, uncertainty and ambiguity; data science is about transforming data into collective action. This year’s Queen’s International Innovation Challenge provides postgraduate students an opportunity to use their analytical skills and creativity to address Food Security — a universal, perpetual challenge that just won’t go away. The competition is open to all students registered in a degree or certificate postgraduate program during the during the the current academic year. What is the relationship between data science and innovation, why engage in such a challenge, why « food security », and what will be the payback for your effort?